Simultaneously expressed miR-424 and miR-381 synergistically suppress the proliferation and survival of renal cancer cells---Cdc2 activity is up-regulated by targeting WEE1

OBJECTIVES: MiRNAs are intrinsic RNAs that interfere with protein translation. Few studies on the synergistic effects of miRNAs have been reported. Both miR-424 and miR-381 have been individually reported to be involved in carcinogenesis. They share a common putative target, WEE1, which is described as an inhibitor of G2/M progression. Here, we studied the synergistic effects of miR-424 and miR-381 on renal cancer cells. METHODS: The viability of 786-O cells was analyzed after transfection with either a combination of miR-424 and miR-381 or each miRNA alone. We investigated cell cycle progression and apoptosis with flow cytometry. To confirm apoptosis and the abrogation of G2/M arrest, we determined the level of pHH3, which is an indicator of mitosis, and caspase-3/7 activity. The expression levels of WEE1, Cdc25, γH2AX, and Cdc2 were manipulated to investigate the roles of these proteins in the miRNA-induced anti-tumor effects. To verify that WEE1 was a direct target of both miR-424 and miR-381, we performed a dual luciferase reporter assay. RESULTS: We showed that the combination of these miRNAs synergistically inhibited proliferation, abrogated G2/M arrest, and induced apoptosis. This combination led to Cdc2 activation through WEE1 inhibition. This regulation was more effective when cells were treated with both miRNAs than with either miRNA alone, indicating synergy between these miRNAs. WEE1 was verified to be a direct target of each miRNA according to the luciferase reporter assay. CONCLUSIONS: These data clearly demonstrate that these two miRNAs might synergistically act as novel modulators of tumorigenesis by down-regulating WEE1 expression in renal cell cancer cells

A novel application of generation model in foreseeing ‘future’ reactions

Deep learning is widely used in chemistry and can rival human chemists in certain scenarios. Inspired by molecule generation in new drug discovery, we present a deep learning-based reaction generation approach to perform reaction generation with the Trans-VAE model in this study. To comprehend how exploratory and innovative the model is in reaction generation, we constructed the dataset by time-split. We applied the Michael addition reaction as the generation vehicle and took the reactions reported before a certain date as the training set and explored whether the model could generate reactions that were reported after the date. We took 2010 and 2015 as the time points for the splitting of the Michael addition reaction respectively. Among the generated reactions, 911 and 487 reactions were applied in the experiments after the respective split time points, accounting for 12.75% and 16.29% of all reported reactions after each time point. The generated results were in line with expectations and additionally generated a large quantity of new chemically feasible Michael addition reactions, which also demonstrated the learnability of the Trans-VAE model for reaction rules. Our research provides a reference for future novel reaction discovery using deep learning

Multi_CycGT: A Deep Learning-Based Multimodal Model for Predicting the Membrane Permeability of Cyclic Peptides

Cyclic peptides are gaining attention for their strong binding affinity, low toxicity, and ability to target “undruggable” proteins; however, their therapeutic potential against intracellular targets is constrained by their limited membrane permeability, and researchers need much time and money to test this property in the laboratory. Herein, we propose an innovative multimodal model called Multi_CycGT, which combines a graph convolutional network (GCN) and a transformer to extract one- and two-dimensional features for predicting cyclic peptide permeability. The extensive benchmarking experiments show that our Multi_CycGT model can attain state-of-the-art performance, with an average accuracy of 0.8206 and an area under the curve of 0.8650, and demonstrates satisfactory generalization ability on several external data sets. To the best of our knowledge, it is the first deep learning-based attempt to predict the membrane permeability of cyclic peptides, which is beneficial in accelerating the design of cyclic peptide active drugs in medicinal chemistry and chemical biology applications